R Notebook
install.packages("Signac") #seurat addon for analyzing chromatinlibrary(hdf5r) #need to read h5 files
library(Seurat)
library(Signac)library(ggplot2)
library(patchwork)setwd("C:\\singlecell_atac")
getwd()[1] "C:/singlecell_atac"Pre-processing workflow
counts <- Read10X_h5("GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_filtered_peak_bc_matrix.h5")# counts is a feature by cell matrix, here features are coordinates of peaks.
counts # 167771 features x 3694 cellsmeta <- read.csv("GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_singlecell.csv.gz",
header=TRUE,
row.names = 1)
meta chrom_assay <- CreateChromatinAssay(
counts = counts,
sep = c(":", "-"), # features (chr1:3670444-3672753, look in counts) are sep by : and ,
genome = 'mm10',
fragments = 'GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_fragments.tsv.gz',
min.cells = 10, # Keep peaks found in ≥10 cells.
min.features = 200 # Keep cells with ≥200 peaks
)Checking for 3694 cell barcodes# Notes:
#
# Make sure fragments and its index are in same folder
# Names of both files should match except for the .tbi
# for example: see belowlist.files("GSE190424_RAW\\Aged")[1] "GSM5723632_Aged_HSC_filtered_peak_bc_matrix.h5"
[2] "GSM5723632_Aged_HSC_fragments.tsv.gz"
[3] "GSM5723632_Aged_HSC_fragments.tsv.gz.tbi"
[4] "GSM5723632_Aged_HSC_singlecell.csv.gz"
data <- CreateSeuratObject(
counts = chrom_assay,
assay = "peaks",
meta.data = meta
)dataAn object of class Seurat
148897 features across 3694 samples within 1 assay
Active assay: peaks (148897 features, 0 variable features)
2 layers present: counts, datadata[[]]
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("EnsDb.Mmusculus.v79") # EnsDb.Hsapiens.v86 for human
BiocManager::install("GenomeInfoDb") #translation between chromosome names
BiocManager::install("biovizBase")library(EnsDb.Mmusculus.v79)
library(GenomeInfoDb)# get all annotated mouse genes from Ensembl-based annotation database
annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
seqlevelsStyle(annotations) <- 'UCSC'
# Note: features have chr1 prefix which is UCSC-style chromosome names. annotationsGRanges object with 1763965 ranges and 5 metadata columns:
seqnames ranges strand | tx_id gene_name
<Rle> <IRanges> <Rle> | <character> <character>
ENSMUSE00001236884 chr3 3508030-3508332 + | ENSMUST00000108393 Hnf4g
ENSMUSE00000676606 chr3 3634150-3634347 + | ENSMUST00000108394 Hnf4g
ENSMUSE00001345708 chr3 3638059-3638230 + | ENSMUST00000108393 Hnf4g
ENSMUSE00001345708 chr3 3638059-3638230 + | ENSMUST00000108394 Hnf4g
ENSMUSE00000149313 chr3 3641223-3641317 + | ENSMUST00000108393 Hnf4g
... ... ... ... . ... ...
ENSMUST00000082414 chrM 10167-11544 + | ENSMUST00000082414 mt-Nd4
ENSMUST00000082418 chrM 11742-13565 + | ENSMUST00000082418 mt-Nd5
ENSMUST00000082419 chrM 13552-14070 - | ENSMUST00000082419 mt-Nd6
ENSMUST00000082421 chrM 14145-15288 + | ENSMUST00000082421 mt-Cytb
ENSMUST00000084013 chrM 9877-10173 + | ENSMUST00000084013 mt-Nd4l
gene_id gene_biotype type
<character> <character> <factor>
ENSMUSE00001236884 ENSMUSG00000017688 protein_coding exon
ENSMUSE00000676606 ENSMUSG00000017688 protein_coding exon
ENSMUSE00001345708 ENSMUSG00000017688 protein_coding exon
ENSMUSE00001345708 ENSMUSG00000017688 protein_coding exon
ENSMUSE00000149313 ENSMUSG00000017688 protein_coding exon
... ... ... ...
ENSMUST00000082414 ENSMUSG00000064363 protein_coding cds
ENSMUST00000082418 ENSMUSG00000064367 protein_coding cds
ENSMUST00000082419 ENSMUSG00000064368 protein_coding cds
ENSMUST00000082421 ENSMUSG00000064370 protein_coding cds
ENSMUST00000084013 ENSMUSG00000065947 protein_coding cds
-------
seqinfo: 22 sequences (1 circular) from mm10 genomeAnnotation(data) <- annotationsComputing QC Metrics
# 1. Look at the fragment length periodicity for all the cells
data <- NucleosomeSignal(object = data)Found 3694 cell barcodes
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Done Processing 18 million lines# Note:
# Signac looks at all fragments in each cell and bins them by length:
# Fragment length Category
# 0–100 bp Nucleosome-free (NFR)
# 180–247 bp Mononucleosomal
# 315–473 bp Dinucleosomal
# ......... .............
# Then calculate mononucleosomal/ nucleosome-free ratio
data$nucleosome_group <- ifelse(data$nucleosome_signal > 4, 'NS > 4', 'NS < 4')
unique(data$nucleosome_group)[1] "NS < 4"# no low-quality cells dominated by mononucleosomal fragments.FragmentHistogram(object = data,
group.by = 'nucleosome_group',
region = 'chr1-1-10000000')
#Note:
# cells exhibit a pattern that is typical for a successful ATAC-seq experiment.
# 2. Calculate the TSS enrichment score for each cell
data <- TSSEnrichment(data, fast = FALSE)# 3. Compute fraction of reads from blacklisted region
# in data[[]] there is a column blacklist_region_fragments
# number of fragments mapped to black list regions
data$blacklist_ratio <- data$blacklist_region_fragments / data$peak_region_fragmentsdata$pct_reads_in_peaks <- data$peak_region_fragments / data$passed_filters * 100 vln <- VlnPlot(
object = data,
features = c('peak_region_fragments', 'pct_reads_in_peaks',
'blacklist_ratio', 'nucleosome_signal', 'TSS.enrichment'),
pt.size = 0.1,
ncol = 5
)
vln
Remove cells that are outliers for these QC metrics
dataAn object of class Seurat
148897 features across 3694 samples within 1 assay
Active assay: peaks (148897 features, 0 variable features)
2 layers present: counts, data# option 1: Set manual thresholdsbased on the violin plot
data_filtered <- subset(
x = data,
subset = peak_region_fragments > 3000 &
peak_region_fragments < 40000 &
pct_reads_in_peaks > 60 &
blacklist_ratio < 0.02 &
nucleosome_signal < 4 &
TSS.enrichment > 2
)
data_filteredAn object of class Seurat
148897 features across 3493 samples within 1 assay
Active assay: peaks (148897 features, 0 variable features)
2 layers present: counts, data# Option 2: Based on quantiles
low_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.02)
hig_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.98)
low_prp <- quantile(data[["pct_reads_in_peaks"]]$pct_reads_in_peaks, probs = 0.02)
high_blr <- quantile(data[["blacklist_ratio"]]$blacklist_ratio, probs = 0.98)
hig_ns <- quantile(data[["nucleosome_signal"]]$nucleosome_signal, probs = 0.98)
low_ts <- quantile(data[["TSS.enrichment"]]$TSS.enrichment, probs = 0.02)print(low_prf) 2%
2280.18 print(hig_prf) 98%
22501.22 print(low_prp) 2%
69.90098 print(high_blr) 98%
0.01488525 print(hig_ns) 98%
0.8913645 print(low_ts) 2%
3.73821 data <- subset(
x = data,
subset = peak_region_fragments > low_prf &
peak_region_fragments < hig_prf &
pct_reads_in_peaks > low_prp &
blacklist_ratio < high_blr &
nucleosome_signal < hig_ns &
TSS.enrichment > low_ts
)
dataAn object of class Seurat
148897 features across 3300 samples within 1 assay
Active assay: peaks (148897 features, 0 variable features)
2 layers present: counts, dataNormalization and linear dimensional reduction
data <- RunTFIDF(data)data <- FindTopFeatures(data, min.cutoff = 'q0')
dataAn object of class Seurat
148897 features across 3300 samples within 1 assay
Active assay: peaks (148897 features, 148897 variable features)
2 layers present: counts, datadata <- RunSVD(data)DepthCor(data)
# Note:
# Component 1 is strongly correlated to sequencing depth (technical variation).
# rather than biological variation
# Remove this componentNon-linear dimension reduction and clustering
data <- RunUMAP(
object = data,
reduction = 'lsi',
dims = 2:30
)
data <- FindNeighbors(
object = data,
reduction = 'lsi',
dims = 2:30
)
data <- FindClusters(
object = data,
algorithm = 3,
verbose = FALSE
)
DimPlot(object = data, label = TRUE, pt.size = 1) + NoLegend()
# Using data_filtered
data_filtered <- RunTFIDF(data_filtered)
data_filtered <- FindTopFeatures(data_filtered, min.cutoff = 'q0')
data_filtered <- RunSVD(object = data_filtered)
DepthCor(data_filtered)
data_filtered <- RunUMAP(
object = data_filtered,
reduction = 'lsi',
dims = 2:30
)Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|data_filtered <- FindNeighbors(
object = data_filtered,
reduction = 'lsi',
dims = 2:30
)
data_filtered <- FindClusters(
object = data_filtered,
algorithm = 3,
resolution = 1.2,
verbose = FALSE
)
DimPlot(object = data_filtered, label = TRUE, pt.size = 1) + NoLegend()
# Note:
# One primary cluster - because data is from aged mice
# Many subclsuters within main clusterMultiple samples
# Function with all the preprocessing steps and informative print messages
prepare_data <- function(count_path, meta_path, fragment_path){
message("Step 1: Reading count data...")
counts <- Read10X_h5(count_path)
message("Count data loaded successfully.")
message("Step 2: Reading metadata...")
meta <- read.csv(meta_path,
header = TRUE,
row.names = 1)
message("Metadata loaded successfully.")
message("Step 3: Creating ChromatinAssay object...")
chrom_assay <- CreateChromatinAssay(
counts = counts,
sep = c(":", "-"),
genome = 'mm10',
fragments = fragment_path,
min.cells = 10,
min.features = 200
)
message("ChromatinAssay created successfully.")
message("Step 4: Creating Seurat object...")
data <- CreateSeuratObject(
counts = chrom_assay,
assay = "peaks",
meta.data = meta
)
message("Seurat object created successfully.")
message("Step 5: Annotating data...")
Annotation(data) <- annotations
message("Annotation completed.")
message("Step 6: Computing QC metrics (nucleosome signal, TSS enrichment, blacklist ratio, % reads in peaks)...")
data <- NucleosomeSignal(object = data)
data <- TSSEnrichment(object = data, fast = FALSE)
data$blacklist_ratio <- data$blacklist_region_fragments / data$peak_region_fragments
data$pct_reads_in_peaks <- data$peak_region_fragments / data$passed_filters * 100
message("QC metrics computation completed.")
message("Step 7: Calculating filtering thresholds...")
low_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.02)
hig_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.98)
low_prp <- quantile(data[["pct_reads_in_peaks"]]$pct_reads_in_peaks, probs = 0.02)
high_blr <- quantile(data[["blacklist_ratio"]]$blacklist_ratio, probs = 0.98)
hig_ns <- quantile(data[["nucleosome_signal"]]$nucleosome_signal, probs = 0.98)
low_ts <- quantile(data[["TSS.enrichment"]]$TSS.enrichment, probs = 0.02)
message("Filtering thresholds calculated.")
message("Step 8: Filtering data based on QC thresholds...")
data <- subset(
x = data,
subset = peak_region_fragments > low_prf &
peak_region_fragments < hig_prf &
pct_reads_in_peaks > low_prp &
blacklist_ratio < high_blr &
nucleosome_signal < hig_ns &
TSS.enrichment > low_ts
)
message("Data filtering completed.")
message("All preprocessing steps completed successfully.")
return(data)
}
aged <- prepare_data("GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_filtered_peak_bc_matrix.h5",
"GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_singlecell.csv.gz",
"GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_fragments.tsv.gz"
)Checking for 3694 cell barcodes
Found 3694 cell barcodes
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Done Processing 18 million linesyoung <- prepare_data("GSE190424_RAW\\Young\\GSM5723631_Young_HSC_filtered_peak_bc_matrix.h5",
"GSE190424_RAW\\Young\\GSM5723631_Young_HSC_singlecell.csv.gz",
"GSE190424_RAW\\Young\\GSM5723631_Young_HSC_fragments.tsv.gz"
)Checking for 5028 cell barcodes
Found 5028 cell barcodes
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Done Processing 25 million lines# Add new column to differentiate between sample aged and young
young$sample <- "young"
aged$sample <- "aged"aged[[]]young[[]]combined_data <- merge(young, aged)
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|===============================================================================| 100%combined_data <- RunTFIDF(combined_data)
combined_data <- FindTopFeatures(combined_data, min.cutoff = 'q0')
combined_data <- RunSVD(object = combined_data)DepthCor(combined_data)
combined_data <- RunUMAP(object = combined_data, reduction = 'lsi', dims = 2:30)Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|combined_data <- FindNeighbors(object = combined_data, reduction = 'lsi', dims = 2:30)combined_data <- FindClusters(object = combined_data, verbose = FALSE, algorithm = 3, resolution = .4)DimPlot(object = combined_data, label = TRUE) + NoLegend()
DimPlot(object = combined_data, label = TRUE, group.by = "sample") + NoLegend()
Data Analysis
# 1. Create a gene activity matrix
# Gene activity based on chromatin accessibility
gene.activities <- GeneActivity(combined_data)
Processed 24766 groups out of 26139. 95% done. Time elapsed: 3s. ETA: 0s.
Processed 26139 groups out of 26139. 100% done. Time elapsed: 3s. ETA: 0s.
| | 0 % ~calculating
|+++++ | 9 % ~03m 26s
|++++++++++ | 18% ~02m 45s
|++++++++++++++ | 27% ~02m 31s
|+++++++++++++++++++ | 36% ~02m 20s
|+++++++++++++++++++++++ | 45% ~01m 51s
|++++++++++++++++++++++++++++ | 55% ~01m 26s
|++++++++++++++++++++++++++++++++ | 64% ~01m 14s
|+++++++++++++++++++++++++++++++++++++ | 73% ~56s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~36s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~18s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 19s
| | 0 % ~calculating
|+++++ | 9 % ~02m 41s
|++++++++++ | 18% ~01m 57s
|++++++++++++++ | 27% ~01m 43s
|+++++++++++++++++++ | 36% ~01m 19s
|+++++++++++++++++++++++ | 45% ~01m 16s
|++++++++++++++++++++++++++++ | 55% ~01m 09s
|++++++++++++++++++++++++++++++++ | 64% ~59s
|+++++++++++++++++++++++++++++++++++++ | 73% ~45s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~31s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 01s# gene.activities
# add the gene activity matrix to the Seurat object as a new assay
combined_data[['RNA']] <- CreateAssayObject(counts = gene.activities)
# Normalize
combined_data <- NormalizeData(
object = combined_data,
assay = 'RNA',
normalization.method = 'LogNormalize',
scale.factor = median(combined_data$nCount_RNA)
)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|combined_data[["RNA"]]Assay data with 21821 features for 7792 cells
First 10 features:
Hnf4g, Zfhx4, Pex2, UBC, 1700008P02Rik, Pkia, Zc2hc1a, Il7, 1700010I02Rik,
Stmn2 combined_data[["peaks"]]ChromatinAssay data with 177359 features for 7792 cells
Variable features: 177359
Genome: mm10
Annotation present: TRUE
Motifs present: FALSE
Fragment files: 2 combined_dataAn object of class Seurat
199180 features across 7792 samples within 2 assays
Active assay: peaks (177359 features, 177359 variable features)
2 layers present: counts, data
1 other assay present: RNA
2 dimensional reductions calculated: lsi, umapDefaultAssay(combined_data)[1] "peaks"# Set RNA as default assay
DefaultAssay(combined_data) <- 'RNA'# Plit expression of genes in each cluster
FeaturePlot(
object = combined_data,
features = c("Hnf4g", "Zfhx4", "Pex2", "Pkia"),
max.cutoff = 'q95',
)
# Set default assay back to peaks
DefaultAssay(combined_data) <- 'peaks'Find differentially accessible peaks between clusters
# peak_region_fragments: total number of reads overlapping any peak per cell
# reflects sequencing depth
# latent.vars = 'peak_region_fragments' adjusts for this
# to remove bias from cells with higher/lower depth
da_peaks <- FindMarkers(
object = combined_data,
ident.1 = rownames(combined_data[[]][combined_data$sample == "aged",]),
ident.2 = rownames(combined_data[[]][combined_data$sample == "young",]),
min.pct = 0.05,
test.use = 'LR',
latent.vars = 'peak_region_fragments'
)
| | 0 % ~calculating
|+ | 1 % ~01h 25m 04s
|+ | 2 % ~01h 06m 54s
|++ | 3 % ~01h 03m 04s
|++ | 4 % ~59m 17s
|+++ | 5 % ~58m 02s
|+++ | 6 % ~56m 07s
|++++ | 7 % ~55m 25s
|++++ | 8 % ~54m 12s
|+++++ | 9 % ~53m 36s
|+++++ | 10% ~58m 05s
|++++++ | 11% ~01h 08m 07s
|++++++ | 12% ~01h 14m 39s
|+++++++ | 13% ~01h 20m 33s
|+++++++ | 14% ~01h 33m 41s
|++++++++ | 15% ~01h 48m 55s
|++++++++ | 16% ~02h 03m 07s
|+++++++++ | 17% ~02h 03m 45s
|+++++++++ | 18% ~02h 12m 26s
|++++++++++ | 19% ~02h 19m 14s
|++++++++++ | 20% ~02h 14m 50s
|+++++++++++ | 21% ~02h 10m 47s
|+++++++++++ | 22% ~02h 07m 55s
|++++++++++++ | 23% ~02h 11m 36s
|++++++++++++ | 24% ~02h 14m 45s
|+++++++++++++ | 25% ~02h 17m 10s
|+++++++++++++ | 26% ~02h 18m 22s
|++++++++++++++ | 27% ~02h 20m 02s
|++++++++++++++ | 28% ~02h 21m 39s
|+++++++++++++++ | 29% ~02h 20m 50s
|+++++++++++++++ | 30% ~02h 18m 44s
|++++++++++++++++ | 31% ~02h 13m 56s
|++++++++++++++++ | 32% ~02h 09m 07s
|+++++++++++++++++ | 33% ~02h 04m 42s
|+++++++++++++++++ | 34% ~02h 03m 43s
|++++++++++++++++++ | 35% ~02h 02m 43s
|++++++++++++++++++ | 36% ~02h 00m 05s
|+++++++++++++++++++ | 37% ~01h 56m 01s
|+++++++++++++++++++ | 38% ~01h 52m 08s
|++++++++++++++++++++ | 39% ~01h 48m 24s
|++++++++++++++++++++ | 40% ~01h 44m 50s
|+++++++++++++++++++++ | 41% ~01h 41m 25s
|+++++++++++++++++++++ | 42% ~01h 38m 17s
|++++++++++++++++++++++ | 43% ~01h 35m 09s
|++++++++++++++++++++++ | 44% ~01h 32m 10s
|+++++++++++++++++++++++ | 45% ~01h 29m 12s
|+++++++++++++++++++++++ | 46% ~01h 26m 20s
|++++++++++++++++++++++++ | 47% ~01h 23m 35s
|++++++++++++++++++++++++ | 48% ~01h 21m 01s
|+++++++++++++++++++++++++ | 49% ~01h 18m 26s
|+++++++++++++++++++++++++ | 50% ~01h 15m 56s
|++++++++++++++++++++++++++ | 51% ~01h 13m 31s
|++++++++++++++++++++++++++ | 52% ~01h 11m 10s
|+++++++++++++++++++++++++++ | 53% ~01h 08m 53s
|+++++++++++++++++++++++++++ | 54% ~01h 06m 45s
|++++++++++++++++++++++++++++ | 55% ~01h 04m 36s
|++++++++++++++++++++++++++++ | 56% ~01h 02m 30s
|+++++++++++++++++++++++++++++ | 57% ~01h 00m 26s
|+++++++++++++++++++++++++++++ | 58% ~58m 26s
|++++++++++++++++++++++++++++++ | 59% ~56m 29s
|++++++++++++++++++++++++++++++ | 60% ~54m 34s
|+++++++++++++++++++++++++++++++ | 61% ~52m 46s
|+++++++++++++++++++++++++++++++ | 62% ~50m 57s
|++++++++++++++++++++++++++++++++ | 63% ~49m 11s
|++++++++++++++++++++++++++++++++ | 64% ~47m 25s
|+++++++++++++++++++++++++++++++++ | 65% ~45m 41s
|+++++++++++++++++++++++++++++++++ | 66% ~43m 60s
|++++++++++++++++++++++++++++++++++ | 67% ~42m 24s
|++++++++++++++++++++++++++++++++++ | 68% ~40m 47s
|+++++++++++++++++++++++++++++++++++ | 69% ~39m 12s
|+++++++++++++++++++++++++++++++++++ | 70% ~37m 39s
|++++++++++++++++++++++++++++++++++++ | 71% ~36m 07s
|++++++++++++++++++++++++++++++++++++ | 72% ~34m 37s
|+++++++++++++++++++++++++++++++++++++ | 73% ~33m 09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~31m 45s
|++++++++++++++++++++++++++++++++++++++ | 75% ~30m 19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~28m 54s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~27m 31s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~26m 09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~24m 48s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~23m 30s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~22m 11s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20m 53s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~19m 37s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~18m 21s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17m 06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15m 52s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14m 40s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13m 28s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12m 17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11m 06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09m 56s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08m 47s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07m 39s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06m 32s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05m 25s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04m 19s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03m 13s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02m 08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01m 04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01h 45m 48shead(da_peaks) # regions that are significant between young and aged samplesplot1 <- VlnPlot(
object = combined_data,
features = rownames(da_peaks)[1], # the peak to plot
pt.size = 0.1, # size of dots per cell
group.by = "sample" # groups to split cells by
)
plot2 <- FeaturePlot(
object = combined_data,
features = rownames(da_peaks)[1],
pt.size = 0.1,
max.cutoff = 'q95'
)
plot1 | plot2
# Find genes nearest to these significant regions
ClosestFeature(combined_data, regions = rownames(da_peaks))NAda_peaks$closest_gene <-ClosestFeature(combined_data, regions = rownames(da_peaks))$gene_name
da_peaks$distance <- ClosestFeature(combined_data, regions = rownames(da_peaks))$distancehead(da_peaks)CoveragePlot(
object = combined_data,
region = rownames(da_peaks)[1],
extend.upstream = 10000,
extend.downstream = 5000,
group.by = "sample"
)
CoveragePlot(
object = combined_data,
region = rownames(da_peaks)[2],
extend.upstream = 10000,
extend.downstream = 5000,
group.by = "sample"
)
plot1 <- VlnPlot(
object = combined_data,
features = rownames(da_peaks)[1],
pt.size = 0.1,
group.by = "sample"
)
plot2 <- FeaturePlot(
object = combined_data,
features = rownames(da_peaks)[1],
pt.size = 0.1,
max.cutoff = 'q95'
)
plot1 | plot2
plot1 <- VlnPlot(
object = combined_data,
features = rownames(da_peaks)[2],
pt.size = 0.1,
group.by = "sample"
)
plot2 <- FeaturePlot(
object = combined_data,
features = rownames(da_peaks)[2],
pt.size = 0.1,
max.cutoff = 'q95'
)
plot1 | plot2
sessionInfo()R version 4.4.3 (2025-02-28 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_India.utf8 LC_CTYPE=English_India.utf8
[3] LC_MONETARY=English_India.utf8 LC_NUMERIC=C
[5] LC_TIME=English_India.utf8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] future_1.67.0 EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.30.0
[4] AnnotationFilter_1.30.0 GenomicFeatures_1.58.0 AnnotationDbi_1.68.0
[7] Biobase_2.66.0 GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[10] IRanges_2.40.1 S4Vectors_0.44.0 BiocGenerics_0.52.0
[13] BiocManager_1.30.26 patchwork_1.3.2 ggplot2_4.0.0
[16] Signac_1.16.0 Seurat_5.3.0 SeuratObject_5.2.0
[19] sp_2.2-0 hdf5r_1.3.12
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.4.3 later_1.4.4
[4] BiocIO_1.16.0 bitops_1.0-9 tibble_3.3.0
[7] polyclip_1.10-7 rpart_4.1.24 XML_3.99-0.19
[10] fastDummies_1.7.5 lifecycle_1.0.4 globals_0.18.0
[13] lattice_0.22-6 MASS_7.3-64 backports_1.5.0
[16] magrittr_2.0.4 sass_0.4.10 Hmisc_5.2-4
[19] plotly_4.11.0 rmarkdown_2.30 jquerylib_0.1.4
[22] yaml_2.3.10 httpuv_1.6.16 sctransform_0.4.2
[25] spam_2.11-1 spatstat.sparse_3.1-0 reticulate_1.43.0
[28] cowplot_1.2.0 pbapply_1.7-4 DBI_1.2.3
[31] RColorBrewer_1.1-3 abind_1.4-8 zlibbioc_1.52.0
[34] Rtsne_0.17 purrr_1.1.0 biovizBase_1.54.0
[37] RCurl_1.98-1.17 nnet_7.3-20 VariantAnnotation_1.52.0
[40] GenomeInfoDbData_1.2.13 ggrepel_0.9.6 irlba_2.3.5.1
[43] listenv_0.10.0 spatstat.utils_3.2-0 goftest_1.2-3
[46] RSpectra_0.16-2 spatstat.random_3.4-2 fitdistrplus_1.2-4
[49] parallelly_1.45.1 codetools_0.2-20 DelayedArray_0.32.0
[52] RcppRoll_0.3.1 tidyselect_1.2.1 UCSC.utils_1.2.0
[55] farver_2.1.2 base64enc_0.1-3 matrixStats_1.5.0
[58] spatstat.explore_3.5-3 GenomicAlignments_1.42.0 jsonlite_2.0.0
[61] Formula_1.2-5 progressr_0.17.0 ggridges_0.5.7
[64] survival_3.8-3 tools_4.4.3 ica_1.0-3
[67] Rcpp_1.1.0 glue_1.8.0 gridExtra_2.3
[70] SparseArray_1.6.2 xfun_0.53 MatrixGenerics_1.18.1
[73] dplyr_1.1.4 withr_3.0.2 fastmap_1.2.0
[76] digest_0.6.37 R6_2.6.1 mime_0.13
[79] colorspace_2.1-2 scattermore_1.2 tensor_1.5.1
[82] dichromat_2.0-0.1 spatstat.data_3.1-9 RSQLite_2.4.3
[85] tidyr_1.3.1 generics_0.1.4 data.table_1.17.8
[88] rtracklayer_1.66.0 httr_1.4.7 htmlwidgets_1.6.4
[91] S4Arrays_1.6.0 uwot_0.2.3 pkgconfig_2.0.3
[94] gtable_0.3.6 blob_1.2.4 lmtest_0.9-40
[97] S7_0.2.0 XVector_0.46.0 htmltools_0.5.8.1
[100] dotCall64_1.2 ProtGenerics_1.38.0 scales_1.4.0
[103] png_0.1-8 spatstat.univar_3.1-4 knitr_1.50
[106] rstudioapi_0.17.1 reshape2_1.4.4 rjson_0.2.23
[109] checkmate_2.3.3 nlme_3.1-167 curl_7.0.0
[112] zoo_1.8-14 cachem_1.1.0 stringr_1.5.2
[115] KernSmooth_2.23-26 parallel_4.4.3 miniUI_0.1.2
[118] foreign_0.8-88 restfulr_0.0.16 pillar_1.11.1
[121] grid_4.4.3 vctrs_0.6.5 RANN_2.6.2
[124] promises_1.3.3 xtable_1.8-4 cluster_2.1.8
[127] htmlTable_2.4.3 evaluate_1.0.5 cli_3.6.5
[130] compiler_4.4.3 Rsamtools_2.22.0 rlang_1.1.6
[133] crayon_1.5.3 future.apply_1.20.0 labeling_0.4.3
[136] plyr_1.8.9 stringi_1.8.7 viridisLite_0.4.2
[139] deldir_2.0-4 BiocParallel_1.40.0 Biostrings_2.74.1
[142] lazyeval_0.2.2 spatstat.geom_3.6-0 Matrix_1.7-4
[145] BSgenome_1.74.0 RcppHNSW_0.6.0 bit64_4.6.0-1
[148] KEGGREST_1.46.0 shiny_1.11.1 SummarizedExperiment_1.36.0
[151] ROCR_1.0-11 igraph_2.1.4 memoise_2.0.1
[154] bslib_0.9.0 fastmatch_1.1-6 bit_4.6.0 ---
title: "R Notebook"
output: html_notebook
---
```{r}
install.packages("Signac") #Seurat addon for analyzing chromatin
```

```{r}
library(hdf5r) #need to read h5 files
library(Seurat)
library(Signac)
```

```{r}
library(ggplot2)
library(patchwork)
```

```{r}
setwd("C:\\singlecell_atac")
getwd()
```
# Pre-processing workflow

```{r}
counts <- Read10X_h5("GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_filtered_peak_bc_matrix.h5")
```

```{r}
# counts is a feature by cell matrix, here features are coordinates of peaks.
counts # 167771 features x 3694 cells
```

```{r}
meta <- read.csv("GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_singlecell.csv.gz",
                 header=TRUE,
                 row.names = 1) 

meta
```


```{r}

  chrom_assay <- CreateChromatinAssay(
  counts = counts,
  sep = c(":", "-"), # features (chr1:3670444-3672753, look in counts) are sep by : and ,
  genome = 'mm10', 
  fragments = 'GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_fragments.tsv.gz',
  min.cells = 10, # Keep peaks found in ≥10 cells.
  min.features = 200 # Keep cells with ≥200 peaks
)
```

```{r}
# Notes:
# 
# Make sure fragments and its index are in same folder
# Names of both files should match except for the .tbi
# for example: see below
```

```{r}
list.files("GSE190424_RAW\\Aged")
```


```{r}

data <- CreateSeuratObject(
  counts = chrom_assay,
  assay = "peaks",
  meta.data = meta
)
```


```{r}
data
```
```{r}
data[[]]
```

```{r}

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("EnsDb.Mmusculus.v79") # EnsDb.Hsapiens.v86 for human
BiocManager::install("GenomeInfoDb") #translation between chromosome names
BiocManager::install("biovizBase")
```

```{r}
library(EnsDb.Mmusculus.v79)
library(GenomeInfoDb)
```

```{r}
# get all annotated mouse genes from Ensembl-based annotation database

annotations <- GetGRangesFromEnsDb(ensdb = EnsDb.Mmusculus.v79)
seqlevelsStyle(annotations) <- 'UCSC'

# Note: features have chr1 prefix which is UCSC-style chromosome names. 
```

```{r}
annotations
```

```{r}
Annotation(data) <- annotations
```

# Computing QC Metrics

```{r}
# 1. Look at the fragment length periodicity for all the cells
data <- NucleosomeSignal(object = data)
```
```{r}
# Note:
# Signac looks at all fragments in each cell and bins them by length:
# Fragment length  Category
# 0–100 bp	Nucleosome-free (NFR)
# 180–247 bp	Mononucleosomal
# 315–473 bp	Dinucleosomal
# .........   .............

# Then calculate mononucleosomal/ nucleosome-free ratio

  
```

```{r}
data$nucleosome_group <- ifelse(data$nucleosome_signal > 4, 'NS > 4', 'NS < 4')
unique(data$nucleosome_group)

# no low-quality cells dominated by mononucleosomal fragments.
```

```{r}
FragmentHistogram(object = data, 
                  group.by = 'nucleosome_group', 
                  region = 'chr1-1-10000000')

#Note:
# cells exhibit a pattern that is typical for a successful ATAC-seq experiment.

```

```{r}
# 2. Calculate the TSS enrichment score for each cell
data <- TSSEnrichment(data, fast = FALSE)
```


```{r}
# 3. Compute fraction of reads from blacklisted region
# in data[[]] there is a column blacklist_region_fragments
# number of fragments mapped to black list regions
data$blacklist_ratio <- data$blacklist_region_fragments / data$peak_region_fragments
```

```{r}
data$pct_reads_in_peaks <- data$peak_region_fragments / data$passed_filters * 100 
```


```{r}
vln <- VlnPlot(
  object = data,
  features = c('peak_region_fragments', 'pct_reads_in_peaks', 
                'blacklist_ratio', 'nucleosome_signal', 'TSS.enrichment'),
  pt.size = 0.1,
  ncol = 5
)
vln
```

# Remove cells that are outliers for these QC metrics

```{r}
data
```

```{r}
# option 1: Set manual thresholdsbased on the violin plot 
data_filtered <- subset(
  x = data,
  subset = peak_region_fragments > 3000 &
    peak_region_fragments < 40000 &
    pct_reads_in_peaks > 60 &
    blacklist_ratio < 0.02 &
    nucleosome_signal < 4 & 
    TSS.enrichment > 2
)

data_filtered
```
```{r}
# Option 2: Based on quantiles
low_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.02)
hig_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.98)

low_prp <- quantile(data[["pct_reads_in_peaks"]]$pct_reads_in_peaks, probs = 0.02)

high_blr <- quantile(data[["blacklist_ratio"]]$blacklist_ratio, probs = 0.98)

hig_ns <- quantile(data[["nucleosome_signal"]]$nucleosome_signal, probs = 0.98)

low_ts <- quantile(data[["TSS.enrichment"]]$TSS.enrichment, probs = 0.02)
```

```{r}
print(low_prf)
print(hig_prf)
print(low_prp)
print(high_blr)
print(hig_ns)
print(low_ts)
```

```{r}
data <- subset(
  x = data,
  subset = peak_region_fragments > low_prf &
    peak_region_fragments < hig_prf &
    pct_reads_in_peaks > low_prp &
    blacklist_ratio < high_blr &
    nucleosome_signal < hig_ns &
    TSS.enrichment > low_ts
)
data
```
# Normalization and linear dimensional reduction

```{r}
data <- RunTFIDF(data)
```

```{r}
data <- FindTopFeatures(data, min.cutoff = 'q0')
data
```

```{r}
data <- RunSVD(data)
```

```{r}
DepthCor(data)
```
```{r}
# Note:
# Component 1 is strongly correlated to sequencing depth  (technical variation).
# rather than biological variation
# Remove this component
```

# Non-linear dimension reduction and clustering

```{r}
data <- RunUMAP(
  object = data,
  reduction = 'lsi',
  dims = 2:30
)

data <- FindNeighbors(
  object = data,
  reduction = 'lsi',
  dims = 2:30
)

data <- FindClusters(
  object = data,
  algorithm = 3,
  verbose = FALSE
)

```


```{r}
DimPlot(object = data, label = TRUE, pt.size = 1) + NoLegend()
```

```{r}
# Using data_filtered

data_filtered <- RunTFIDF(data_filtered)
data_filtered <- FindTopFeatures(data_filtered, min.cutoff = 'q0')
data_filtered <- RunSVD(object = data_filtered)

DepthCor(data_filtered)
```

```{r}
data_filtered <- RunUMAP(
  object = data_filtered,
  reduction = 'lsi',
  dims = 2:30
)
data_filtered <- FindNeighbors(
  object = data_filtered,
  reduction = 'lsi',
  dims = 2:30
)
data_filtered <- FindClusters(
  object = data_filtered,
  algorithm = 3,
  resolution = 1.2,
  verbose = FALSE
)

DimPlot(object = data_filtered, label = TRUE, pt.size = 1) + NoLegend()
```

```{r}
# Note:
# One primary cluster - because data is from aged mice
# Many subclsuters within main cluster
```

# Multiple samples

```{r}
# Function with all the preprocessing steps and informative print messages
prepare_data <- function(count_path, meta_path, fragment_path){
  
  message("Step 1: Reading count data...")
  counts <- Read10X_h5(count_path)
  message("Count data loaded successfully.")
  
  message("Step 2: Reading metadata...")
  meta <- read.csv(meta_path,
                   header = TRUE,
                   row.names = 1)
  message("Metadata loaded successfully.")
  
  message("Step 3: Creating ChromatinAssay object...")
  chrom_assay <- CreateChromatinAssay(
    counts = counts,
    sep = c(":", "-"),
    genome = 'mm10',
    fragments = fragment_path,
    min.cells = 10,
    min.features = 200
  )
  message("ChromatinAssay created successfully.")
  
  message("Step 4: Creating Seurat object...")
  data <- CreateSeuratObject(
    counts = chrom_assay,
    assay = "peaks",
    meta.data = meta
  )
  message("Seurat object created successfully.")
  
  message("Step 5: Annotating data...")
  Annotation(data) <- annotations
  message("Annotation completed.")
  
  message("Step 6: Computing QC metrics (nucleosome signal, TSS enrichment, blacklist ratio, % reads in peaks)...")
  data <- NucleosomeSignal(object = data)
  data <- TSSEnrichment(object = data, fast = FALSE)
  data$blacklist_ratio <- data$blacklist_region_fragments / data$peak_region_fragments
  data$pct_reads_in_peaks <- data$peak_region_fragments / data$passed_filters * 100
  message("QC metrics computation completed.")
  
  message("Step 7: Calculating filtering thresholds...")
  low_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.02)
  hig_prf <- quantile(data[["peak_region_fragments"]]$peak_region_fragments, probs = 0.98)
  low_prp <- quantile(data[["pct_reads_in_peaks"]]$pct_reads_in_peaks, probs = 0.02)
  high_blr <- quantile(data[["blacklist_ratio"]]$blacklist_ratio, probs = 0.98)
  hig_ns <- quantile(data[["nucleosome_signal"]]$nucleosome_signal, probs = 0.98)
  low_ts <- quantile(data[["TSS.enrichment"]]$TSS.enrichment, probs = 0.02)
  message("Filtering thresholds calculated.")
  
  message("Step 8: Filtering data based on QC thresholds...")
  data <- subset(
    x = data,
    subset = peak_region_fragments > low_prf &
      peak_region_fragments < hig_prf &
      pct_reads_in_peaks > low_prp &
      blacklist_ratio < high_blr &
      nucleosome_signal < hig_ns &
      TSS.enrichment > low_ts
  )
  message("Data filtering completed.")
  
  message("All preprocessing steps completed successfully.")
  
  return(data)
}

```


```{r}
aged <- prepare_data("GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_filtered_peak_bc_matrix.h5",
                     "GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_singlecell.csv.gz",
                     "GSE190424_RAW\\Aged\\GSM5723632_Aged_HSC_fragments.tsv.gz"
                     )
```
```{r}
young <- prepare_data("GSE190424_RAW\\Young\\GSM5723631_Young_HSC_filtered_peak_bc_matrix.h5",
                     "GSE190424_RAW\\Young\\GSM5723631_Young_HSC_singlecell.csv.gz",
                     "GSE190424_RAW\\Young\\GSM5723631_Young_HSC_fragments.tsv.gz"
                     )
```

```{r}
# Add new column to differentiate between sample aged and young
young$sample <- "young"
aged$sample <- "aged"
```

```{r}
aged[[]]
young[[]]
```


```{r}
combined_data <- merge(young, aged)
```

```{r}
combined_data <- RunTFIDF(combined_data)
combined_data <- FindTopFeatures(combined_data, min.cutoff = 'q0')
combined_data <- RunSVD(object = combined_data)
```

```{r}
DepthCor(combined_data)
```

```{r}
combined_data <- RunUMAP(object = combined_data, reduction = 'lsi', dims = 2:30)
combined_data <- FindNeighbors(object = combined_data, reduction = 'lsi', dims = 2:30)
```

```{r}
combined_data <- FindClusters(object = combined_data, verbose = FALSE, algorithm = 3, resolution = .4)
```


```{r}
DimPlot(object = combined_data, label = TRUE) + NoLegend()
```

```{r}
DimPlot(object = combined_data, label = TRUE, group.by = "sample") + NoLegend()
```

# Data Analysis

```{r}
# 1. Create a gene activity matrix or scores
# Estimate gene expression-like values from open chromatin regions (peaks)
# Each entry epresents the gene activity score for that gene in that cell.

gene.activities <- GeneActivity(combined_data)
```

```{r}
# gene.activities
# add the gene activity matrix to the Seurat object as a new assay
combined_data[['RNA']] <- CreateAssayObject(counts = gene.activities)

```

```{r}
# Normalize
combined_data <- NormalizeData(
  object = combined_data,
  assay = 'RNA',
  normalization.method = 'LogNormalize',
  scale.factor = median(combined_data$nCount_RNA)
)
```
```{r}
combined_data[["RNA"]]
```

```{r}
combined_data[["peaks"]]
```
```{r}
combined_data
```


```{r}
DefaultAssay(combined_data)
```
```{r}
# Set RNA as default assay 
DefaultAssay(combined_data) <- 'RNA'
```


```{r}
# Plit expression of genes in each cluster

FeaturePlot(
  object = combined_data,
  features = c("Hnf4g", "Zfhx4", "Pex2", "Pkia"),
  max.cutoff = 'q95',
)
```

```{r}
# Set default assay back to peaks
DefaultAssay(combined_data) <- 'peaks'
```


# Find differentially accessible peaks between clusters

```{r}
# peak_region_fragments: total number of reads overlapping any peak per cell
# reflects sequencing depth
# latent.vars = 'peak_region_fragments' adjusts for peak_region_fragments
# This remove bias from cells with higher/lower depth

# min.pct = 0.05 - only test peaks present in ≥5% of cells in either group

da_peaks <- FindMarkers(
  object = combined_data,
  ident.1 = rownames(combined_data[[]][combined_data$sample == "aged",]),
  ident.2 = rownames(combined_data[[]][combined_data$sample == "young",]),
  min.pct = 0.05,# 
  test.use = 'LR',
  latent.vars = 'peak_region_fragments'
)
```
```{r}
head(da_peaks) # regions that are significant between young and aged samples
```


```{r}
plot1 <- VlnPlot(
  object = combined_data,
  features = rownames(da_peaks)[1],             
  pt.size = 0.1,                   
  group.by = "sample"             
)

plot2 <- FeaturePlot(
  object = combined_data,
  features = rownames(da_peaks)[1],
  pt.size = 0.1,
  max.cutoff = 'q95'
)
plot1 | plot2

```


```{r}
# Find genes nearest to these significant regions
ClosestFeature(combined_data, regions = rownames(da_peaks))

```

```{r}
da_peaks$closest_gene <-ClosestFeature(combined_data, regions = rownames(da_peaks))$gene_name
da_peaks$distance <- ClosestFeature(combined_data, regions = rownames(da_peaks))$distance
```

```{r}
head(da_peaks)
```

```{r}
CoveragePlot(
  object = combined_data,
  region = rownames(da_peaks)[1],
  extend.upstream = 10000,
  extend.downstream = 5000,
  group.by = "sample"
)
```
```{r}
CoveragePlot(
  object = combined_data,
  region = rownames(da_peaks)[2],
  extend.upstream = 10000,
  extend.downstream = 5000,
  group.by = "sample"
)
```

```{r}
plot1 <- VlnPlot(
  object = combined_data,
  features = rownames(da_peaks)[1],            
  pt.size = 0.1,                   
  group.by = "sample"              
)

plot2 <- FeaturePlot(
  object = combined_data,
  features = rownames(da_peaks)[1],
  pt.size = 0.1,
  max.cutoff = 'q95'
)
plot1 | plot2
```


```{r}
plot1 <- VlnPlot(
  object = combined_data,
  features = rownames(da_peaks)[2],            
  pt.size = 0.1,                   
  group.by = "sample"              
)

plot2 <- FeaturePlot(
  object = combined_data,
  features = rownames(da_peaks)[2],
  pt.size = 0.1,
  max.cutoff = 'q95'
)
plot1 | plot2
```
```{r}
sessionInfo()
```

